{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 拿去花开通前后火车票CR\n",
    "- 时间段：开通前后各三个月\n",
    "- 分母：有uv的天数\n",
    "- 分子：订单数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 数据准备\n",
    "- 读取导出的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"b:\\trn_rate_1114.txt\",sep='\\t',\n",
    "                  dtype={\"uid\":str,\"active_date\":str,\"rate_before\":float,\"rate_after\":float})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开通前平均： 0.237601\n",
      "开通后平均： 0.544395\n"
     ]
    }
   ],
   "source": [
    "df.head()\n",
    "from numpy import mean, median\n",
    "print('开通前平均： %f' % mean(df['rate_before']))\n",
    "print('开通后平均： %f' %mean(df['rate_after']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 看下0的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总数据量： 369807\n",
      "开通前无订单： 209957\n",
      "开通后无订单： 97825\n",
      "开通前后无订单： 66373\n"
     ]
    }
   ],
   "source": [
    "print('总数据量： %d' % len(df))\n",
    "print('开通前无订单： %d' % len(df[df['rate_before'] == 0]))\n",
    "print('开通后无订单： %d' % len(df[df['rate_after'] == 0]))\n",
    "print('开通前后无订单： %d' % len(df[(df['rate_before'] == 0) & (df['rate_after'] == 0)]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 开通后为0（无订单）的情形减少。下面看下分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set_palette('bright', desat=.6)\n",
    "sns.set_context(rc={'figure.figsize': (10, 5) } )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Gc4Zht/hD4q6BrjqmEhGpn/CKvGxpZeEAZOM72HYNamlFRGan8Iq87KyVjgHYKS7yDVpa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\nLiISOBW5iEjgVOQiIoFTkYuIBO7/A6lYLGv4s+AzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e49828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "ax1=sns.distplot(df['rate_before'],color='r')\n",
    "ax2=sns.distplot(df['rate_after'],color='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 不是很清晰，看下取对数后的图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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crIu+j7tITZLQ01ykWjCAZ+68thtNE9uyj3C8/SZaMAiTJtFw9XVnNECoq9YF\nQzXTiZbBbA0eTsg1basiz/nimxphseoONCd015ETUSd0gPOLp7DmyOqT64x6A16ONByhNHcYFs1y\nciIvSehCHoqK6NTX43zurzjffB0zK5vGe+6De+9NaDIHWBfYjwWdcfqAhF43Fmr7habRzTkW/fB/\ngMnFUwH49HBonveDdQcwMRkcXrxjoCuU0MvrD8YqVNFLSQ1ddMqys4ycO27DusPAP3QYnq/egZmT\nm/A4/GaAz4MHOUvvT6aW/Mmq2qu5t+dkk8vRriX084pDo1DXHFnNVUPnnHwgOjgnNOS0RGroIkxq\n6KJD9rffJO/yi7DuMGiaOQv3fQ8kJZkDbA8ewY2vV7afA9QWh2rouYeOd+m8vpl9Kc0Zytqjawia\nwZMPRAe5Qgk9w5pBYUbRyT7pIn1JDV1E5veT9bNHyHz8UczMTGqfehqtri6pITW3n5/bhQFFXa1F\nx1Nt33x8Dht99h3t8rmT+0/lReMfHGs8xv66vWTbssl35J/cPzB7INtObCVoBtFjsLCH6J3k/7xo\nQ6urI/eWG8h8/FH8w0dQtfBDvF/4YrLDYnlgFwCT9Chnt+ppdJ3K0mIK9h9pNf1uNM4vDs2D87t1\nv6baW83gnNKT3RUBBroG4w14qXBXxDRk0btIDV20ou/bS8YzT6PX1OC9ag51v38yrk0skVYNmmdr\n23vEY/r4p/9zBmsFjO+FD0SbHR/an2LjAHnlx6nqQk+Xy4dcyZ/yR1PtrcZld3HhwIta7R/Q/GC0\n7gD9Ms9sbVbRe3Wa0JVSOvAEMAHwAncbhrGzxf5bgW8BfmAT8HXDMILxCVdEI1JXxIjdEE9jW7US\nx2svQzBI/Y9/ivvBb0GLWmAyvR/YSh1e7rLOaFUz7W2OD+0PQOGew11K6P2zS1h266c8syVyN9OB\n2ad6ujSPLhXpJ5omlxsAp2EY04CHgV8371BKZQD/BVxsGMYFQC4wJx6BijgyTezvvo3z5RcwnU7c\n996P+5sP9ZhkDvCKPzSXyU3W+EwpkCjHS8MJfW9s+9APyA49VzggPV3SWjRNLjOAhQCGYaxSSrX8\n+PcC0w3DaGxRnqejwvLzM7FaEz/laXcUFbmSHUL3uJxtN7X3WpwW+Nvf4JNPoG9f9G9+k8yiIjIj\nHR+p3AjbOrzeacc7G9u+BV2ZrY+pD3p4t34zytKPC3KHn6yhRzq3J3I6rTjDr9szrhSA4gPH2r13\nzSK9/yIH3PGwAAAYXklEQVSdU1TkYrxvNAAn/Ed75fu2N8bcE0XzG5ED1LT4OaCUshqG4Q83rRwF\nUEo9CGQD73dUWFVVY0e7e4yiIhcVFcnt1dFdzrq2n6meCK9FP1RO3i//F8v+fQQGDcZ9572YThfU\neSIef3q5LpeTugjXau96kcrw+PxtjqkLtD7mJd9nuPHxBf1c6uu9HZ7b0zidVjweP77w665zOvFk\nZ5BbVt7uvWsW6f0X6ZyKijoy/aE+7mUVu3vd+7Y3/64lSrQfeNEk9FqgZWm6YRgnf5PCbey/BEYB\nNxmGEd2y5iKpbEsWk/PAvejHj+ObeB6em24BhyPZYbWxzF/Gj5peB+AmW+wXf044TeP40P6UbNmL\npclHwB6bGSP7OPuQYc2Q0aJpLpqEvgK4FnhRKTWV0IPPlp4i1PRygzwM7QXq68n+jx+T8czTmDYb\nni98Ed/0GT2mvTxomtzsfop1wf2UaHlsCpajo/Fz+xdQevuLMPcmx4f2Z+Cm3RTsP0bFiPZ77LT3\nADQSTdMYkD2QA7X7YhGi6KWiSeivAZcppVYCGjBPKXUboeaVtcBdwDJgiVIK4HeGYbwWp3jFGbDs\nMMiZ+2Wsu3fhH3MWtY/Px7b+s2SH1cpnwX0sCmwlj0y2m0co1frwlPN2plhiNy1vslWGH4z22XO4\nw4TeVUNyStlZXUaNt5pcR2Ln2BE9Q6cJPVzrvu+0zdtbfC+Dk3oB25LF5NxzB3pdLY33P0jDD/8f\nOBw9KqF7TT9v+T/HiY3lmd9jkBYaCdmbuylGcnxo6C+NWPd0Kc0NfejtrdnDhL4p0DwlukyScRqw\nrf6E3Nu+iNbkpfaJP9Lw0//uke3lSwLbqcHNA7aLGawXoGlayiVzOFVDL9wT24Q+NGcYAHtr98S0\nXNF79I5+X6LbbEs/xPnW6wQLCqh57kX8502O6rz25kmPF4/pY0lgOzk4+bb90oReO9Hcedk05Lti\nntCba+h7anbHtFzRe0gNPVUFg9jfeSuUzHNzqX5zUdTJPBk2Bw/RRIDpluFkaz3vr4dYqxheQu6R\nE2RXVMeszKG5w4FQk4tIT5LQU1FTE87n/opjyWKChYU0fuNfCYxSyY6qQ+ubZ1LspVPjdlXZzPEA\njFm8NmZlDs4ZgobGnlqpoacrSegpRt+9i8wnHsP2+Qb8w4bT8OC3MQv6JDusDjWaTWwLHqG/lkux\nnpPscBLCmD0Rv83K2e+uBjM2QzccFgcDsgdKDT2NSUJPFaaJ869PUzB7BpaDB2iaPAX3vV+HrKxk\nR9apzcFyAgQ5V49+nvPezpudyc6Z4yk4cIz+W/fGrNyhucM43HAIt98dszJF7yEPRVOAfvQI2Q89\ngGPxewRz8/B8ZS7+cyclO6yorQ+GJpRKl+aWZpuvnMLoJesYu/BTDo+NTT/70tyhLCtfyr7avYwu\nGNNqX6SBSnPHdj4Lp+g9pIbey9nffpP8WVNxLH6PplkXU7X0k16VzCvMOozgUQZoefTVQzNMLPCt\nbPOVivZPHEVdUR6jP1yH1dMUkzJLc0NdF6WnS3qShN5bud1kf+8hcu+8Ha2xkbqf/ZKaF14jWNJ7\nFn+oMz38ybecAEFmW0YnO5yEMy06Wy4/H0eDh7PfXRWTMktzTg0uEulHEnovpO/bS/6Vs8n465/x\nn3U2VYuX4bn7PtB7x//OoBlkR/AoX/M8xxGzlpn6CM6z9NJl5c7Qxutn4HZlMuvJN+hn7D/j8oae\nrKHvOuOyRO8jbei9jKVsB7lfvA7L4UO477iL+p/+DDIykh1W1NYF9nOLZz7HzNB0qcO1Ir5gTf1h\n6u0tVl0/dTrv/Oir3PiD+Vz3k6d57qnv4s7N7vZ1SnNKARktmq4kofcils2byLvlevTjx6n/j//G\n/fUHkx1SlyzY9Cce9fyJY2Ydk/QhlOp9mKyXYolilfpUbUe3rVpJObD6krFMXbyZa7/zO17+w8ME\nu7kITLbdRVFGX2lDT1O942/0NOd8ZgGZ//kT8q+5FK2yEs+NN2Nmd78WlyxLD37IYbOG6fow5tqm\ncqFlJE4tNvOB93afXnwWu84awKBdx7js1y+cUd/00tyhHKw7QIOvodNj/UE/Zoz6wYvkkxp6L2DZ\ntZOMp+dDUxOeL38F/6TzgcTPt9IVp8e2N1jJ+43vkI2Da60TkhRVD6ZrLPrSVG6av4SzF66mpn8f\nVs29oltFXTjwItYcWc3ftz3DPePvj3iMx+9h8f73+OGy7/G983/Av076zplEL3oILdGfzhUVdb2i\nOtBTlsWyLVlM7le/BMFgqH/5+HOSHdJJHS1B15JpmtzkeYrFgW3MtU5lUho9AG1egi5amXVubv7z\ncnKPnuCNR+5k58zOP/xO70te6a5k0rNjyXPk8+ntG7Fb7MCpfuiV7kr+b8Oj1DbVAjAsdzirvrI+\n6hhjraf8rvVk4SXoOp16VJpcejD7P98KJXMT3Hfc1aOSeWda9iH/V+8LLA5sQ2n9mJhmg4e6qtGV\nwVtfPh+fzcIVP3uWgnffx7ZqZbsPVSPpk9GHr551B4caynl5xwtt9i/a+w61TbVcPOgSLh18Obtr\ndrGruiyWL0MkiST0Hsrx/N/IuXsu2Oy47/4agTFjkx1StzSaTbzqX48NC7fYzkvJ+c1j7URxHkuv\nm4jT4+Oqv69E9we6XMb95zyITbfx2Lrf4PGf+ivqaMMR1h/7jP5ZJVw1dA5zhl8PwHt7F8UsfpE8\n0obe0wSDZP3Xf5D5+KME8/Ko+dtLWLdtTXZU3XLcrOdZ3ypq8XCNZRyFWu97kJssWycNZcDuY4xZ\nv48L317PR9e3P/o30rOUxZNgUr/zWXV4JTP+cT53jL2LHEcu7+1biInJFaVXo2s6lwy5HID39y3k\n/nMeiNvrEYkhCb0H0WprcD3wNRwL38E/fAS1z71AYPjIXpnQ1wX287x/DV78TNIHM9vSs6fv7XE0\njY+un0Th4WrGr95FgyuDldMu6FIR14+4kaZAE+uOreVXa39BQUYBB+sOMNA1iLF9zgagX2Y/zu07\nkVWHV8papClAmlx6CMuWzeRdNgvHwndomnkR1e9+QGD4yGSH1WUBM8hPvG/yV/8nANxuncJc2zSs\nWvf6Vaczn8PGm/NmUZOfxbTFmzn3laUQDEZ9vk23cevo27lm2HVoWqi5xWFxMGfo9a2avi4bciX+\noJ+PDiyJx8sQCSS9XNqRyCfvjuf/huv730Zzu/HOvpSmK64GS89PgKf3cikLHuPb3hdZGiijUMvm\nHuvMtJnfvD1d7eUSSV5FHTc/9QEZDV5q+hWw5crJ1PTvg89p54gazK3lBW3OmR/l/Gxzx87j84oN\nXPrShdw08haevOxPZxRrd0gvl85F28tFmlySyeMh+0ffJ+PZBQRzcql9agH6saPJjqpL/GaAVcE9\nvOHfyALfCpoIcKVlLLMso8jU7MkOLyVUF7l46WuXcO72E6gl65j+14Un9/ltFgIXzqZp9mXdXvh7\nXOEEBmYP4r19C/H4PTitzliFLhJMEnqSZP7iv3H+4zksh8oJlAzAPffOXpfM9/qPc0njb9ltHgdg\ngJbHLxw3cq1lPH8JN7mI2KgucvHetVfw4Te+QOma7Tjq3TjrGjn3tY/J+eB9bJ+tpfH+BzH7dH11\nKk3TuHb4DTy58fd8eOADrhp6TcTjZD71nk/a0BMtECDjid+T+eivsBwqp2nKNBof/BZmYWGyI+uS\nfcFKLjrxG3abx7nVej6vOO9jQ+a/c511gnRNjCNfppOyWeew+ZpprP3yJSz4yw9pmjUbvbqKjGcX\ngL97zTs3jLgRgDd2vhrLcEWCSQ09gSybN+H6zoPY1q8jmJ2N++ZbCYw9O9lhdckC30oqzQYeb1rC\nCRq5xjKOp5y3JzustOXPcOC99nq0hnpsaz/F8eZreG+8ucvlnNN3IkNySlm0913cfjcZ1tAMnrur\nd/LctmdYfnAp5fXl3DfhAXIdubF+GSJGJKHHmfOZBeBuxPH+ImzLP0YLBvFNnIT3uht7/ARbkWY4\nbJnMr3dMYDbptzBFT+S58Wb08gPYVy4nUDoUJp3XpfM1TeP64Tfy2Prf8MG+95kz/Dp2VZcx59XL\nqfRUoms6QTPIW7te5/az/iVOr0KcKWlyiadAANuqlWT9z39h//gjzPx8Gu+5D89tc3t8Mj+d3wyw\nJrCXx1rUzK929q6/LlLVAt9KFmhr+dut59LksGJ5+R8U7DvS5XKuHxlqdnl03a94ZceLfOmtG6n0\nVPLIBT9j590HGeQawoaKdZRVGbF+CSJGJKHHiW3lcvIvvRDnyy+g+Xx4r55Dw3d/QECN6fzkHqLR\nbGJD4ADP+9bw06a3ec6/mhrcXGsZz+XWs5IdnjhNdZGLxTdNxt7k59qfLMDq9nbp/LP7jGP24Ev5\nvGID9y++m/11+/j+5B9x34QHyLZlc+PIL6Kh8drOV/AHz6wrpogPaXKJMduqlWT++hfYl34IgO+8\nyXivmoOZ2/PbHYNmkI3BchYHtrHYv43VwT0ECQ0byMTORZZRXGgZRR8tK8mRivbsHDeIDdNHcs7K\nMq785T949wdfIWCPbs55TdP4xzWvsLFiPe/sfps8Zz73Tzg1HcAg12CmlVzAykPLebXsRW4edWu8\nXoboJknosWCa2JZ/TPa/PYR1104A/MNH4r3mWoKDe/ZUsceCtXwQMPggsI0lAYPjZj0AGhpDtAJG\n68WM0YsZrBWgR7GykEi+5VdNoG+ND/XRevrsPcy7P7idY6MGAW2XwnN+FvrXMzfU/VDTNM7pO5Fz\n+k6MWPY1w65jX+1ePj2ymsKMIqaUTGNX9U40NDJtmZxfPIUsm3zgJ4uMFG1HVKPXvF4cb79Bxp/n\nY1v7KQB+NYamSy8nMHRYAqLsOtM0+WXTe3wePMiG4AEOmFUn9xVrOcy2jOYyyxgusire9G/ssKxY\njIJMdUm7RxMmceFTb3LOG8sJ6hq1xQXU9itAr64mu9aN3ePD67SRk12If8Qo6n/5G4KDOp7auLkf\neo23hsfW/Zqappo2x+Q7Cvjzlc8wY8CFUYcqI0U7F+1IUUno7Wj3TWaaWD9bg+PVl3C+9jJ6ZSUA\n3iuvxj9qdI+rkR8KVrM2uI8dwaPsCB5jRWDnySSuozFcK2KM3p/RejElWm6X+pBLQu9csu/RoLIj\nTF6ylbzKOrLqPJgaNGY58WbYcHh8ZDY0oQWDmJpG0+xL8cy9k6bLruAZ49kOyz1UX87bu98kz5FL\n38xidE2n0n2cTw6tIEiQK0qv4srSazirz1isFhv9s0oozIg81kISeuckoZ+hlm8yrb4O2+pPsL+/\nCPv7i7Ac2A9AMCsL//lTaJp6QY8YGOQzA2wOHmJ1YA+rg3v4NLCnVQ0cIJ9Mhuh9GKMXM04fQJbW\nveHikPxk1Rv0pHtk8QUwdY2g5VTT2TzzPKwb12PdWYbtszUABPoVs3tUX46XFlNfmIsvw4E3O4Pa\nfgXUFBfgy2x/aoD9tft4pexFyusPttquoTGx33lcOuRyppfM4Ny+k05OMSAJvXMxS+hKKR14ApgA\neIG7DcPY2WL/tcD/A/zA04Zh/LGj8np0QjdN9KNHsBjbyTu0F/e6z7FuXId10+dogdAiA8GcXAIj\nRuA7dxKBUaMTMomW3wxwwmzgBI1UmvVUmg1Umg2cMBuoNOs5ZNawO1jBluBhfJxaDCELB7MsI5ls\nKeUsvYThehHDtEKe8a+KSVw9KVn1VL3lHvmmTqdoZznj316J+nA9GbXtLzBd2zePiuEDqCwtprp/\nIbXFBbjzsnHnZOLOycLvtFPhrmDbia3UeGsImH6CZpDVhz8hYIbenxbNwpCcUkblK8aXnM1A51BG\n5I1kZP4ocuxd+0sxHcQyod8IXGcYxh1KqanADwzDuD68zwZsA84HGoAVwBzDMNqdlORMErp29Cia\nuxFME80MQtAMTSdqhv8Nf2mEf/b70Tye0DluN1pjI5rbHfpqqEc7UYleUYF+vAJ31VGO1x5Ga6gj\nqEFQAxMIWHV8/fsTGDgI39Ch+EtKMHWdICbBcB+QIObJ//wE8ZkB/ATxEwh/ndrWhJ9600sDTTSa\nXupposH00mg2UU/o3wa81JteGsP7aul83U4bFvpqLoZofRiq92GoXkgh2XH9xegtySqZess98k2d\nfuoH0yTrRC393v2AjHovtiY/TncTrqoG8irrKahyk11Z225ZfpsVTzi5e3Ky8DntDCgcQWWmxsf5\n1azIrmSts5KtluPUWnxtztdNjUzNRgZ2MjQ7fZ2FZFgycFqdOHUHmZbQvye/t4S+z9CdOC1OMnQH\nGeFjLLoFTdPR0dB1Pfy9jqbraGjomo6uW9Ag/H14OzqaFvqu+XxN09C1U8cOcBaj6ad1FDj9963l\nzxYLwZIBbY+JQixnW5wBLAQwDGOVUqrlELQxwE7DMKoAlFLLgQuBl7oacGfs77xN7h23xbpYAPw6\njPwOVER8OB8EysNfq0J/o8SRBtix4sCKXbPi0pz0JYdszU4WDrI0R/hfO9nh712ak1wy0KVWI7rp\n9N4vTcCBEcXtHp9R7yHveB25J+rJqWrE2ejF2djU4t8mXIePU7TncPiMrZQAXw5/NavIhO2FsK0o\n9O+OPlDlNKl1NIW/4LD/BL4eOJv0w8vg5x907ZyGb3+Pxof/PT4BEV1CzwFaPs4OKKWshmH4I+yr\nAzrscF1U5Ope1vmXW0NfcWAFjsWlZCFER4rCXzOTHUiCZIW/4iWajsW1gKvlOeFkHmmfC6iOUWxC\nCCG6IJqEvgK4GiDchr6pxb5twEilVIFSyk6ouUUmwhZCiCToSi+X8YSaeOcBE4FswzDmt+jlohPq\n5fJ/8Q1ZCCFEJAnvhy6EECI+ZHIOIYRIEZLQhRAiRUhCF0KIFCHT53ZAKTUaWA30Mwyj8+GaaUYp\nlQs8R2g8gh34tmEYad/LqbPpMkRIeKT500Ap4AD+yzCMN5MaVA+llOoLfAZcZhjG9vaOkxp6O5RS\nOcCvifvY0F7t28AHhmHMAu4ApIdTyA2A0zCMacDDhN5Hoq3bgUrDMGYCVwKPJzmeHin8wfcU4O7s\nWEnoESilNGA+8EOgMcnh9GS/JfRGg9Bfe/JXTEir6TKArq3YnD5eAprHwWuEJvgTbf0K+ANwqLMD\n077JRSl1F/DQaZv3Ac8bhrFRKZWEqHqedu7TPMMw1iiligk1vXwr8ZH1SB1NlyHCDMOoB1BKuYCX\ngR8nN6KeRyl1B1BhGMYipdQPOjte+qFHoJTaCTRP6DwV+NQwjOiXYEkjSqlxwPPAdw3DeDfZ8fQE\nSqnfAKsMw3gx/PNBwzAGJjmsHkkpNQh4DXjCMIynkx1PT6OU+pjQxK8mcA6wg9Dst0ciHZ/2NfRI\nDMMY0fy9UmovcHnSgunBlFJnEfqz+UuGYXS8Xl16WQFcC7wYYboMEaaU6ge8BzxgGEYX5y1MDy0r\nkkqpj4D72kvmIAldnJmfA07gd+GmqZrmufLT3GvAZUqplZyaLkO09UMgH/h3pVRzW/pVhmF0+vBP\nRCZNLkIIkSKkl4sQQqQISehCCJEiJKELIUSKkIQuhBApQhK6EEKkCEnootdSSl0U7pubiGs9rZTa\noZS6VSn1YSKuKURXSUIXIjp3AGcbhvEP4KLkhiJEZDKwSPR6SqlRhCZTKwAagG+G55gZCPyN0OCV\nTcCsjobgh2fY/DMwECgBPgbmAm8QGiD0aXiwEEqp1YZhTFFKXQk8AtiAPcA9hmFUhkcYryY0XHum\nYRjHYv7ChTiN1NBFKngOeMwwjPGEJhB7WSnlAH4HvBDe/jIwoJNyrgE2hKe9HQlMAyYahnEdgGEY\n5xiG8fXw91OUUkXA/wBXGIZxLrAI+EWL8t41DENJMheJIgld9HbZwAjDMF6Fk9PVngAUcBnwbHj7\na0B1RwWFm1PeV0p9C/g90CdcfnumAIOBD5VSG4AHCH0QNFvdnRckRHdJk4vo7XRCzSEtaYTe2wG6\nUGlRSj0IfJFQ881i4OwIZbdkAZY31+CVUk7A1WK/zEkiEkpq6KK3qwV2KaVuBAjPblgMbAbeB24L\nb78KyOukrMuApwzD+Bunpiu1RDguoJSyEqqBTwu34UNosYb/PbOXI0T3SUIXqeB24JtKqU2EljG7\n0TCMJkILbtyklFoPfIlOmlyAR4GfKKXWEVoTdCUwNMJxbwAbw+XdSWia3E3AROA7MXg9QnSLzLYo\nUpZS6pvAYsMwtiqlJgJ/NAxjUrLjEiJeJKGLlBVuZvkfIEhovdNvEHpoGXEpL8MwzklcdELEniR0\nIYRIEdKGLoQQKUISuhBCpAhJ6EIIkSIkoQshRIqQhC6EECni/wOAQGgmGvZZoAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13892048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "# 去除0的数据\n",
    "df = df[(df.rate_before !=0) & (df.rate_after !=0) ]\n",
    "df['log_before'] = df['rate_before'].apply(lambda x: math.log(x) if x != 0 else 0)\n",
    "df['log_after'] = df['rate_after'].apply(lambda x: math.log(x) if x != 0 else 0)\n",
    "ax1=sns.distplot(df['log_before'],color='r')\n",
    "ax2=sns.distplot(df['log_after'],color='g')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 看一下比值的对数（中心点为0）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['rate_rate'] = df.apply(lambda row:math.log(row['rate_after']/row['rate_before']),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Oc+iQxcSJCSI6Mq/HTZ3qlWO09x48mtyVbxoa4Cc/KSYcdpk5U0syfhg3ziEW\nc1m7NsTR4MzxUmhyVz568MEI1dU2Z5+ta8n4JRSCSZMS1NdbPPecjq8IEk3uyhd1dfCLXxQRi7lc\ncIH22v2UHCmjM1aDRZO78sX//E8R+/bZfOMbjfTq5Xc0ha283KWyMsGKFWG2bdMTq0GhyV31uI8+\ngt/+toj+/R1uvrnR73AUqUsBa+89KDS5qx73hz9EOHjQ4utfb9Jee4447TSHsjKXxx6L0Kift4Gg\nyV31qIYG+M1viujVy+XGGzWL5IpIBK65pokPP7RZvFhPrAaBJnfVY6qqItxxR5S9e20mTUqwcGGE\nqiotA+SK5Lo+WpoJBk3uqsc4Drz8cohQyOWcc3SETK4ZO9bhzDMTLF0a4r339MRqvtPkrnrMxo02\nH35oc+aZiZy5vJs6pqoqQmWlg+tafOc7Uaqq9JtVPtPkrnrM8uXeFPcZM3QFwlw1fnyC4mKX118P\n4ehSP3lNk7vqEcbYbNkSorIywZAheqWlXFVcDBMmJDhwwGLjRk0P+UxfPdUjHnjA+3qvvfbcN326\n9xqtWKGLieUzTe6q2x04AE88EaGszGXcOP2un+uGDHEZOdJh8+aQLgWcx9od0CoiNjAPGA80ADcZ\nY7ambL8M+C8gDvzeGPPb5vvXAYeaH7bdGKMXyS5Q991XRH29xcyZTYS0M5gXPv3pONu2FWnvPY9l\nMlvhSiBqjJkmIlOBucAVACISAe4BJgOHgRUishA4CFipF81WhWn3bot584o48USHs8/Wkky+GDfO\noV8/lzfeCHHwIDq6KQ9lUpaZASwCMMasBialbBsLbDXG1BpjGoHlwDl4vfxSEVksIkuaPxRUAZoz\np5gjRyz+4z8aKC72OxqVqVAIpk+P09Rk6aSmPJVJz70PXk88KSEiYWNMPM22OqAvUA/8HHgAGAU8\nLyLSvE9aZWWlhMPB+ApYXh7zO4Ss6Upb1qyBp56CiRPhtttKeOCBLAbWCbFY1N8Asqy723PBBfDX\nv8IDD0T57nej3frhrO+Z7MskuR8CUqO1U5J0y20x4ACwGa9H7wKbRWQfMBiobu1JamvrOxJ3ziov\nj1FTU+d3GFnR2bZUVUWIx+HXvy4CbKZMaeCee/wd/hiLRamrC86lhnqqPVOnhlm2LMy8eUf5539u\n6pbn0PdM158znUzKMiuASwCayysbUrZtBEaJSH8RKcIryawCZuPV5hGRIXg9/D2dDV7ln4ULw+ze\nbTN5cpzj2eoBAAAL/ElEQVTKSh3Xnq/OOSdOUZHLr35VRFxXjMgrmST3BcBREVmJd/L0DhG5TkRu\nNsY0Ad8CXsBL6r83xuwGfgf0E5HlwOPA7LZKMipY1q+3WbkyzODBDp/7nL7s+axvX/jSl5rYscPm\n2Wd1tch8YrlubvSqamrqciOQLir0r5hbttjMnFmKZcG//msjJ56YGy+rlmU675xz4kyd2gsRh6VL\n67GzPDum0N8zWXjOtJMRdBKTypr6erjppiiNjRbXXNOUM4lddc3w4S5XXx1n48YQjz2mvfd8ocld\nZYXrwne+E2XjxhDTp8eZMEFnogbJ977XQGmpy5w5xRw61P7jlf80uausePjhCI8/HuGMMxJcdpnW\n2YOkqirCX/8a5pxz4nz4oc2NN5b4HZLKgCZ31WXLl4f4zneKKStz+e1vjxDWb+6BdO65CcrKHJYv\nD7Fli6aOXKevkOqSd9+1mD27BMuCBx88QkWF1tmDKhKBK66Ik0hY3HhjlI8+8jsi1RZN7qrTamvh\nsstKOXDA4vOfb2LLFluv3BNwp57qMGNGnE2bQtx+e5QcGWyn0tDkrjqlsRFmzy7hww9tzj8/zuTJ\negK1UFx2WZyzz47z3HMRfvrTIr/DUa3Q5K46zHXhzjuLWbEizGmnJbjoIj2BWkhCIXjggaOcdJLD\n3LnFzJlTpD34HKTJXXWI68IPflDM/PlFjB+f4Nprm7I+qUXlvr/8JcxXvtLIgAEOv/pVMVdcUcKD\nD2pJLpfo21JlLJGAb3+7mHvvLeLkkxM89NARivRbecHq3x9uu62RIUMcVq8OM39+hMZGv6NSSZrc\nVUbq6+Hmm6M89FARp52WYOHCIwwapN/FC10sBv/yL42MGOHw5pshvvrVEuqDscBr3tPkrtq1e7fF\n9Om9ePbZCCNHOnzhC0385S9hHRmjACgpga9/vZExYxK89FKYq64qpaZGr73qN03uqlWu6y3dO2tW\nKbt320yZEufmmxsp0QmKqoWiIvja15q4+uom1q4NcfHFpWzerOnFT/rXV2lt2mRz6aVw000l1NVZ\nXHllE1dfHdfZp6pV4TDce+9R/v3fG9i1y+aii0p57LGwjqTxiS75m2X5vHxpIgHf/W4xy5eH2LrV\nu+ThqFEJrroqzoAB+f3y6JK/PWv9epsnn4zQ0GBx+ukJHnnkCAMHpj+G8vk901IuLfmr/TDF/v3w\nyCNFPPhghOpq78tcZWWCz3wmxMiRTVhaPlUddMYZDhUVjTz2WIS33goxdWov7rijkZtvbiQarEvZ\n5iztuWdZvvRCXNfrXVVVRXj66QhHj1qUlrqcfnqC6dMTDB7s5nzvsCOC1BbIn/Y4DqxeHWLZshD7\n9tkMGuRw662NfOUrTfTq5T0mX94zmcilnrsm9yzL5QO1qQneeCPEiy+GmD8/wv79Xi/9hBMcpk9P\nMGlSgtLSY4/PlwSSiSC1BfKvPUeOwEsvhVm5MkRjo9eRmDQpwQ9/2MD06b1y9j3TUbmU3LUsE0BN\nTVBdbbFnj82ePRabNtm8+WaIdetCfPSRdxwUF7tMnJjgjDMSiDg6y1R1q5ISuPTSODNnxlm+PMyq\nVSFeeSXMjBlhJk6Ez362iAsvjDNmjENER9hmRbs9dxGxgXnAeKABuMkYszVl+2XAfwFxvAtk/7a9\nfdLRnnvHHDkCe/ZYbN9us22b/fHPbdtsqqstEolPfpiXlzuMHu0g4jBqVPtvonzrHbYlSG2B/G9P\nPA7vvGPzj3/YvPJKmKYm7/7iYpexYx2GDnUYONBl0CCXgQOP/T5okEO/fuTseaB867lfCUSNMdNE\nZCowF7gCQEQiwD3AZOAwsEJEFgLTW9sn2+Jx+Mc/LFyX4/4lHbsv/WNauy/dtvT7Wcfd168f1NaG\ncF2v3tjU5MXY2Gg1//RuNzVZNDXR/C/192OPTd3W0AD79ll88IHFBx/YHDqU/uju3dvlpJNcTjjB\noazMpU8fl/Jyl6FDHR2frnJGOAzjxzuMH+9w6aVh1qxpZNs2m/fes9mwwfum2ZriYpeBA13693cp\nKXEpLYWSEpeSEigtPfYz3f0lJS7RKIRCLrbNcf+AT9xn227aD5J0fWLX9QYn7N//yR0s69gHUsvf\n+/RxKSvr6F+wfZkk9xnAIgBjzGoRmZSybSyw1RhTCyAiy4FzgGlt7JNVs2dHWbQo177Hlbb/kE6w\nLO+A7dPH68HEYjBggMOAAV4CP+EEVxO4yjulpXDmmQ5nnuktG+04cPgwHDpkpfw7/vbBgxZ79lg0\nNeViF753hx4dCrm8+uphTj45u8WLTJJ7H+Bgyu2EiISNMfE02+qAvu3sk1ZrXy3a8/zzndkrX1kt\nfgK03sPJjiCNWwtSWyBY7QlSWzrKoqMfCJnI5DTaISCWuk9Kkm65LQYcaGcfpZRS3SyT5L4CuASg\nuX6+IWXbRmCUiPQXkSK8ksyqdvZRSinVzToyWuZ0vO8PNwATgd7GmPtTRsvYeKNl7k23jzFmU/c1\nQymlVKqcmcSklFIqe3TqilJKBZAmd6WUCiBN7kopFUC6tkw3EZExwGvAQGNMXs4TF5G+wMN48xaK\ngG8ZY1b5G1XHdGYpjFzVPCP898BwoBiYY4xZ6GtQXSQiJwJrgVn5PuhCRL4LXI73XplnjPmdn/Fo\nz70biEgfvCUXGvyOpYu+BbxkjDkX+Bpwr7/hdMrHy2cAd+G9Lvnqn4F9xphPAxcB/8/neLqk+cPq\nPuCI37F0lYicB5yNt/TKucBJvgaEJvesExELuB/4HpDv14G/B+/NB963vHz8BnLc8hlAty2F0QOe\nAP6z+XcLb7G+fPZz4DfAP/wOJAs+izefZwHwLPBnf8PRskyXiMiNwB0t7t4JPGaM+ZuI+BBV57TS\nlhuMMa+LyCC88sy/9XxkXdbhpTBylTHmIwARiQFPAv/hb0SdJyJfA2qMMS80lzPy3QBgGHApMAJY\nKCJjjDG+jTXX5N4FzTW14+pqIrIVuLE5WQ4CFuPN3M1p6doCICKnAY8B3zbGLOvxwLouUEthiMhJ\neL3DecaY+X7H0wWzAVdELgQmAFUicrkx5n2f4+qsfcAmY0wjYETkKFAOfOBXQJrcs8wYc3LydxHZ\nAXzGt2C6SETG4ZUCvmiM+Zvf8XTSCuAy4I/5vhSGiAzE6yx8wxjzkt/xdIUx5uMOj4i8DNySx4kd\nYDnwryJyNzAY6IWX8H2jyV215Ud4y/X9srnEdNAY0y3r8nejBcAsEVnJseUz8tX3gDLgP0UkWXu/\n2BiT9yck850x5s8icg6wBu9c5m3GmISfMenyA0opFUA6WkYppQJIk7tSSgWQJnellAogTe5KKRVA\nmtyVUiqANLmrgiMiI0Sk2xd1EpGl3f0cSrVGk7sqRMOAyh54nvN64DmUSkvHuatAaV6d76dACNgP\nJIB+eLMGHzXG3CUibwEjgT8YY24TkbuALzTv8wLwnbbWBBGRGrxlagcBk/GWFD4VGAgY4PPAT4Db\ngTXGmCkichHwAyACbAe+bozxdQajCjbtuasgGg2cj5eoHzXGTMW7WPutIjIA+CbwRnNivwg4Ey9J\nnwF8CvhyO///AODHxpgJwDSgsXlJ4ZOBEuASY8w3AZoTeznwY+CzxpgzmuP6SVZbrFQLuvyACiJj\njDkI/FxEZorIt/F61kV4a36kuhCYgtcTBy8578rgOV5rfqJXRGSfiNwGjAFGAb1bPHYKUAEsbV7G\nIfmtQqluo8ldBdERABGZi1d+mQ88g5fIrRaPDQG/MMbc3bxPPzJYJz25nouIXI5Xbvkl8L94vfp0\nz7HcGHN58z5Rjl+pUqms07KMCrJZwM+MMU/gXRnnU3iJNs6xjs0S4Csi0ltEwngfAld34DkuBP5o\njPlf4H285Z1DzdsSzf/na8A0ERndfP9/Aj/rfLOUap/23FWQ/Qh4SEQOAHuBN/AupLAe6CciDxlj\nviIi4/EScAjvqk1/6MBz/BaYLyLX4F1WcXXzcwD8CfgbXk1/Nt6ywyHgPbxL5inVbXS0jFJKBZD2\n3JVqQURKgFWtbP4vY8zCnoxHqc7QnrtSSgWQnlBVSqkA0uSulFIBpMldKaUCSJO7UkoFkCZ3pZQK\noP8PLKgMHeNNTE0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc28c898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=sns.distplot(df['rate_rate'],color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
